Real-Time Spoken Language Translation Using Associative Processors

This paper proposes a model using associative processors (APs) for real-time spoken language translation. Spoken language translation requires (1) an accurate translation and (2) a realtime response. We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates a sentence utilizing examples effectively and performs accurate structural disambiguation and target word selection. This paper will concentrate on the second requirement. In TDMT, example-retrieval (ER), i.e., retrieving examples most similar to an input expression, is the most dominant part of the total processing time. Our study has concluded that we only need to implement the ER for expressions including a frequent word on APs. Experimental results show that the ER can be drastically speeded up. Moreover, a study on communications between APs demonstrates the scalability against vocabulary size by extrapolation. Thus, our model, TDMT on APs, meets the vital requirements of spoken language tra...

• Type: Conference
• Year: 1994
• Where: ANLP
• Authors: Kozo Oi, Eiichiro Sumita, Osamu Furuse, Hitoshi Iida, Tetsuya Higuchi
• Keywords: ANLP 1994, Real-time Spoken Language, Spoken Language Translation, Transfer-Driven Machine Translation